Laura Matzen, Michelle A Leger, Geoffrey Reedy (Sandia National Laboratories)

Binary reverse engineers combine automated and manual techniques to answer questions about software. However, when evaluating automated analysis results, they rarely have additional information to help them contextualize these results in the binary. We expect that humans could more readily understand the binary program and these analysis results if they had access to information usually kept internal to the analysis, like value-set analysis (VSA) information. However, these automated analyses often give up precision for scalability, and imprecise information might hinder human decision making.

To assess how precision of VSA information affects human analysts, we designed a human study in which reverse engineers answered short information flow problems, determining whether code snippets would print sensitive information. We hypothesized that precise VSA information would help our participants analyze code faster and more accurately, and that imprecise VSA information would lead to slower, less accurate performance than no VSA information. We presented hand-crafted code snippets with precise, imprecise, or no VSA information in a blocked design, recording participants’ eye movements, response times, and accuracy while they analyzed the snippets. Our data showed that precise VSA information changed participants’ problem-solving strategies and supported faster, more accurate analyses. However, surprisingly, imprecise VSA information also led to increased accuracy relative to no VSA information, likely due to the extra time participants spent working through the code.

View More Papers

How Different Tokenization Algorithms Impact LLMs and Transformer Models...

Ahmed Mostafa, Raisul Arefin Nahid, Samuel Mulder (Auburn University)

Read More

Preventing and Detecting State Inference Attacks on Android

Andrea Possemato (IDEMIA and EURECOM), Dario Nisi (EURECOM), Yanick Fratantonio (EURECOM and Cisco Talos)

Read More

Evaluating Disassembly Ground Truth Through Dynamic Tracing (abstract)

Lambang Akbar (National University of Singapore), Yuancheng Jiang (National University of Singapore), Roland H.C. Yap (National University of Singapore), Zhenkai Liang (National University of Singapore), Zhuohao Liu (National University of Singapore)

Read More

A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired...

Kimberly Redmond (University of South Carolina), Lannan Luo (University of South Carolina), Qiang Zeng (University of South Carolina)

Read More